Tatiana Krikella is a CANSSI Distinguished Postdoctoral Fellow for 2024–2026.

Post-Graduate Stories

Tatiana Krikella Will Help to Develop “Multi-scale Estimation Methods for Infectious Disease Management”

As a 2024 CANSSI Distinguished Postdoctoral Fellow, Tatiana Krikella will take part in a comprehensive program that involves teaching, interdisciplinary or applied collaboration, professional development, and a research project that aims to aid in public health management of infectious disease outbreaks by providing real-time, understandable estimates of essential disease parameters, particularly two key parameters—the effective reproduction number and the serial interval—that are not well-understood. She will work under the supervision of Professors Jane Heffernan and Hanna Jankowski (York University) and Professor Quazi Rahman (Trent University).

Program: CANSSI Distinguished Postdoctoral Fellowship

Project Focus Areas

Tatiana Krikella will participate in a major research project whose scientific objective is to aid in public health management of infectious disease outbreaks by providing real-time, understandable estimates of essential disease parameters. The research team will initially focus on two key parameters, the effective reproduction number and the serial interval. Numerous estimators of these quantities exist, yet their properties—especially behaviour under model mis-specification—are not well-understood. The statistical objective is therefore to study these estimators in depth and then to develop new methods that will allow for multi-scale estimation. The key techniques for the multi-scale component will be either hierarchical Bayesian or machine learning pathways. The project will rely on data provided by Public Health.

Tatiana will also participate in a wide range of other activities designed to give her a broadly based training experience. In addition to her collaborations at York University and Trent University, she will interact with public health and government research groups in various Canadian jurisdictions, including Public Health Ontario, the Public Health Agency of Canada, and the National Research Council of Canada. She will also teach a course during each year of her fellowship, mentor a graduate student, and present the team’s work at group meetings and conferences. This experience will be supplemented by interdisciplinary research and communication training and participation in the York University POLARIS EDI training course.

Tatiana Krikella’s supervisors: Jane Heffernan, Hanna Jankowski, and Quazi Rahman.

Getting to Know Tatiana

Tatiana Krikella is completing a PhD in biostatistics at the University of Waterloo under the supervision of Joel A. Dubin. She has also participated in a cross-disciplinary project with Anindya Sen in the Department of Economics that uses statistical learning methods to predict the hospital readmission of cardiac patients.

Tatiana’s stated goal is to become a professor. She views the CANSSI Distinguished Postdoctoral Fellowship as a valuable step in that direction.

“Through [the CDPF program], I wish to become a more well-rounded researcher and delve deeper into areas of statistics that I have not explored to date … Without CANSSI, I may not have had the opportunity to explore topics beyond my depth of knowledge.”

She also points to the teaching opportunities that her fellowship will provide.

“I have been a teaching assistant for many years, having even taught my own course, and this is an aspect of academia that I enjoy. I find great fulfillment in helping students reach their full potential. The CANSSI fellowship will help to refine my teaching skills, and give me the opportunity to mentor a graduate student; a possibility that I look forward to.”

Through [the CDPF program], I wish to become a more well-rounded researcher and delve deeper into areas of statistics that I have not explored to date.

About the Supervisors

Jane Heffernan

Jane Heffernan is a professor of infectious disease modelling in the Mathematics and Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity. Jane was recently elected to the Royal Society of Canada’s College for New Scholars. Jane’s Modelling Infection and Immunity Lab tackles essential questions in mathematical epidemiology and in-host pathogen dynamics, using mathematical and computational modelling to ascertain key characteristics of pathogens, individual hosts, and populations that allow for disease spread and to determine public health and medical intervention strategies that will be needed to contain or eradicate infectious disease. Her work is funded by NSERC, CIHR, MITACS, NRC, CIRN, and government and industry contracts.

Hanna Jankowski

Hanna Jankowski is a full professor in the Department of Mathematics and Statistics at York University. Her main research interests are in nonparametric and semi-parametric statistics.

A considerable amount of her recent work is in shape-constrained maximum likelihood estimation. The key idea behind this approach is to strike a balance between parametric methods and purely nonparametric methods by specifying a shape (e.g., convexity) of the function of interest. Unlike kernel smoothing methods, shape-constrained methodology does not require a choice of bandwidth, as it is automatically locally adaptive.

She is also interested in statistical inference for random sets. Random sets are a natural approach to the analysis of shapes, and do not require user-defined landmarks, making them more natural in certain contexts. A closely associated work is the Bayesian approach to principal curves described here, which shows an application to the analysis of SPECT medical imaging data.

Her interest in random sets has recently led to research in effective dose estimation in the multivariate setting. When more than one agent is present, the effective dose estimator is essentially a random set. Thus far, her research has focused on developing practical confidence regions in the multivariate setting.

She is also interested in applied and collaborative work. Some recent/current projects include flightpath recovery for migrating songbirds and clustering of the H3N2 flu virus.

Quazi Rahman

Quazi Rahman is an assistant professor in the Department of Computer Science at Trent University.

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